Incarceration experiences in a cohort of active injection drug users
Bibliographic record
Abstract
BACKGROUND: Incarceration has been associated with a number of health-related harms among injection drug users (IDU). However, little is known about the prevalence and correlates of incarceration among community-based samples of IDU. METHODS: We examined the prevalence and correlates of recent incarceration among IDU in the Scientific Evaluation of Supervised Injecting (SEOSI) cohort examined between 1 July 2004 and 30 June 2006 using generalised estimating equations (GEE). RESULTS: A total of 902 individuals were included in the analysis, of whom 255 (28.72%) were female and 536 (59.42%) reported a history of incarceration. In a multivariate GEE model, recent incarceration was associated positively and independently with a number of high-risk drug using behaviours, including syringe sharing. CONCLUSIONS: An alarmingly high proportion of active IDU reported recent incarceration and injecting while incarcerated. Recent incarceration was associated independently with syringe sharing. These findings add further evidence to repeated demands for an expansion of appropriate harm-reduction measures in Canada's prisons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".